{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "50452016",
   "metadata": {},
   "source": [
    "# Visualize Nearest Neighbor Search on Image Search"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e50707a0",
   "metadata": {},
   "source": [
    "This notebook shows you how to visualize the ANNS (Nearest Neighbor Search) process in reverse image search using [Feder](https://github.com/zilliztech/feder) and [Towhee](https://towhee.io/), Feder is a tool for visualizing ANNS index files, currently it supports the index files from *Faiss* and *Hnswlib*. More information about feder you can learn from \"[Visualize Your Approximate Nearest Neighbor Search with Feder](https://zilliz.com/blog/Visualize-Your-Approximate-Nearest-Neighbor-Search-with-Feder)\" and \"[Visualize Reverse Image Search with Feder](https://zilliz.com/blog/Visualize-Reverse-Image-Search-with-Feder)\"\n",
    "\n",
    "First, we visualize the quantization-based indexing (IVF_FLAT) and graph-based indexing (HNSW) when search images, then compare the index whether to normalize the vector and whether to add object detection, and finally visualize the cross-model retrieval process, which we can use text to search for images and visualize the process of searching for \"a white dog\" and \"a black dog\"."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "345292a9",
   "metadata": {},
   "source": [
    "## Preparation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba03d953",
   "metadata": {},
   "source": [
    "- **Install dependencies**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5e12444",
   "metadata": {},
   "source": [
    "First to install the related dependencies, such as [feder](https://github.com/zilliztech/feder), [towhee](https://github.com/towhee-io/towhee), [hnswlib](https://github.com/nmslib/hnswlib) and numpy.\n",
    "\n",
    "> Please install [faiss](https://github.com/facebookresearch/faiss) with [conda](https://docs.conda.io/projects/conda/en/latest/user-guide/install/index.html) in your env, such as`conda install -c pytorch faiss-cpu`, or you can try `pip install faiss-cpu`(not official)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ddcfcdda",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip available: \u001b[0m\u001b[31;49m22.2.2\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.0\u001b[0m\r\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\r\n"
     ]
    }
   ],
   "source": [
    "! python -m pip -q install federpy towhee hnswlib numpy"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "685feaed",
   "metadata": {},
   "source": [
    "- **Prepare the data**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d90ba8e",
   "metadata": {},
   "source": [
    "Then to download the image dataset, which is a subset of the [ImageNet dataset](https://www.image-net.org/) (100 classes, 10 images for each class) and it is available via [Github](https://github.com/towhee-io/examples/releases/download/data/reverse_image_search.zip). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e854fc9f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
      "                                 Dload  Upload   Total   Spent    Left  Speed\n",
      "  0     0    0     0    0     0      0      0 --:--:-- --:--:-- --:--:--     0\n",
      "100  119M  100  119M    0     0  3122k      0  0:00:39  0:00:39 --:--:-- 4251k\n"
     ]
    }
   ],
   "source": [
    "! curl -L https://github.com/towhee-io/examples/releases/download/data/reverse_image_search.zip -O\n",
    "! unzip -q -o reverse_image_search.zip"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97b355e0",
   "metadata": {},
   "source": [
    "This imageset is the same data in [Build a Milvus Powered Image Search Engine in Minutes](../reverse_image_search/1_build_image_search_engine.ipynb) and [Deep Dive into Real-World Image Search Engine with Towhee](../reverse_image_search/2_deep_dive_image_search.ipynb) notebook. Next to get all images in the **train** directory, `images` will be used for Feder to display data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "199d6342",
   "metadata": {},
   "outputs": [],
   "source": [
    "import glob\n",
    "\n",
    "images = glob.glob('train/*/*.JPEG')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f1ec47f",
   "metadata": {},
   "source": [
    "## Visualize reverse image search with Towhee and Feder"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eef64b3f",
   "metadata": {},
   "source": [
    "The process of visualize reverse image search is mainly divided into three steps: \n",
    "\n",
    "1. First generate feature vector of the image dataset, and get image list for Feder to show images by `mediaUrls`.\n",
    " \n",
    "2. Insert the vector into FAISS/HNSWLib, then create the index and save the index file.\n",
    "\n",
    "3. Feder reads the index file and visualizes the process of searching for images. And Feder support `searchByID` or `SearchByVec` for the quey image.\n",
    "\n",
    "<img src=\"feder_towhee.png\" width=\"600px\">"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c5be3a1",
   "metadata": {},
   "source": [
    "- **Generate Image Feature Vector**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5a067d9",
   "metadata": {},
   "source": [
    "We use `image_embedding.timm` operator to generate image vectors, this operator is form [Towhee hub](https://towhee.io/image-embedding/timm) and it supports a variety of image models, including `vgg16`, `resnet50`, `vit_base_patch8_224`, `convnext_base`, etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c807d294",
   "metadata": {},
   "outputs": [],
   "source": [
    "from towhee import pipe, ops\n",
    "import numpy as np\n",
    "\n",
    "p_embed = (\n",
    "    pipe.input('src')\n",
    "        .flat_map('src', 'img_path', lambda x: glob.glob(x))\n",
    "        .map('img_path', 'img', ops.image_decode.cv2())\n",
    "        .map('img', 'vec', ops.image_embedding.timm(model_name='resnet50'))\n",
    "        .output('vec')\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1408a6a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "vectors = p_embed('train/*/*.JPEG').to_list()\n",
    "vectors = np.array(np.squeeze(vectors), dtype=\"float32\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22d1ca42",
   "metadata": {},
   "source": [
    "- **Train and add data to Faiss**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1cbeec7",
   "metadata": {},
   "source": [
    "The `save_faiss_index` function is defined here to insert the vector data into Faiss and save an index (IVF_FLAT) file. Before adding to faiss, these 1000 pieces of data are also used for training, and the IVF_FLAT index parameter is `nlist=128`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "af79d187",
   "metadata": {},
   "outputs": [],
   "source": [
    "import faiss\n",
    "\n",
    "def save_faiss_index(vec, file_name):\n",
    "    dim = vec.shape[1]\n",
    "    nlist = 128\n",
    "    faiss_index = faiss.index_factory(dim, 'IVF%s,Flat' % nlist)\n",
    "    faiss_index.train(vec)\n",
    "    faiss_index.add(vec)\n",
    "\n",
    "    faiss.write_index(faiss_index, file_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "35600e1b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING clustering 1000 points to 128 centroids: please provide at least 4992 training points\n"
     ]
    }
   ],
   "source": [
    "save_faiss_index(vectors, 'faiss.index')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0dbc1e03",
   "metadata": {},
   "source": [
    "- **Add data to HNSWLib and create index**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ddff188f",
   "metadata": {},
   "source": [
    "Similarly, the `save_hnswlib_index` function here is used to insert the vector into HNSWLib and save the index (HNSW) file, where the index parameters are `ef_construction=30`, `M=6`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7a4e5470",
   "metadata": {},
   "outputs": [],
   "source": [
    "import hnswlib\n",
    "\n",
    "def save_hnsw_index(vec, file_name):\n",
    "    dim = vec.shape[1]\n",
    "    max_elements = vec.shape[0]\n",
    "    hnsw_index = hnswlib.Index(space='l2', dim=dim)\n",
    "    hnsw_index.init_index(max_elements=max_elements, ef_construction=30, M=6)\n",
    "    hnsw_index.add_items(vec)\n",
    "\n",
    "    hnsw_index.save_index(file_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0a89794f",
   "metadata": {},
   "outputs": [],
   "source": [
    "save_hnsw_index(vectors, 'hnswlib.index')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d06f873",
   "metadata": {},
   "source": [
    "- **Search in Faiss and visualization**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b903bc42",
   "metadata": {},
   "source": [
    "Next to define the `get_faiss_feder` function, which uses Feder to read the index file from Faiss, the `mediatType` is set to `img`, and the `mediaUrls` is `images`. The `images` is declare before, which is used to display all images in Feder. In addition, the search parameters of faiss are k=5, nprobe=6."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "24c6bbfd",
   "metadata": {},
   "outputs": [],
   "source": [
    "from federpy.federpy import FederPy\n",
    "\n",
    "def get_faiss_feder(faiss_index_file_name):\n",
    "    viewParams = {\n",
    "        \"width\": 950,\n",
    "        \"height\": 600,\n",
    "        \"mediaType\": \"img\",\n",
    "        \"mediaUrls\": images,\n",
    "        \"fineSearchWithProjection\": 1,\n",
    "        \"projectMethod\": \"umap\"\n",
    "    }\n",
    "    faiss_feder = FederPy(faiss_index_file_name, 'faiss', **viewParams)\n",
    "    faiss_feder.setSearchParams({\"k\": 5, \"nprobe\": 6})\n",
    "    return faiss_feder"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "190b00bb",
   "metadata": {},
   "source": [
    "Before search vector with Feder, let's take a look about Faiss' **[IVF_FLAT](https://github.com/facebookresearch/faiss/wiki/Faiss-indexes#cell-probe-methods-indexivf-indexes)** index:\n",
    "\n",
    "- The feature space is partitioned into nlist cells.\n",
    "- The database vectors are assigned to one of these cells thanks using a quantization function (in the case of k-means, the assignment to the centroid closest to the query), and stored in an inverted file structure formed of nlist inverted lists.\n",
    "- At query time, a set of nprobe inverted lists is selected\n",
    "- The query is compared to each of the database vector assigned to these lists."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27c4edbe",
   "metadata": {},
   "source": [
    "Next, we can get the faiss feder and visualize the index. Below is an example of retrieving a picture with id 40, you can see the basic information of the index (1000 vectors, divided into 128 clusters) and the process of retrieving the information (finding the 6 closest clusters) are listed on the left. And there are three options of \"Coarse Search\", \"Fine Search (Distance)\" and \"Fine Search (Project)\", you can choose to change the style of the board.\n",
    "\n",
    "The entire board is divided into 128 (nlist=128) clusters, each cluster displays the corresponding pictures (9 pictures in the cluster are randomly displayed), and the highlighted part is the 6 (nprobe=6) clusters closest to the query picture during the retrieval process, the circle is the cluster center point closest to the query image."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "714f08b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<!DOCTYPE html>\n",
       "<html>\n",
       "\n",
       "<head>\n",
       "  <meta charset=\"utf-8\" />\n",
       "  <meta name=\"viewport\" content=\"width=device-width,initial-scale=1\" />\n",
       "  <meta name=\"theme-color\" content=\"#ffffff\" />\n",
       "  <title>Feder</title>\n",
       "</head>\n",
       "\n",
       "<body style=\"background: #000\">\n",
       "  <div id=\"feder-container-988511\" />\n",
       "</body>\n",
       "\n",
       "<script type=\"module\">\n",
       "\n",
       "\n",
       "import { Feder } from \"https://unpkg.com/@zilliz/feder\"\n",
       "// console.log(Feder)\n",
       "\n",
       "const mediaUrls = 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       "const mediaCallback = (rowId) => rowId in mediaUrls ? mediaUrls[rowId] : null\n",
       "\n",
       "const feder = new Feder({\n",
       "    filePath: \"faiss.index\",\n",
       "    source: \"faiss\",\n",
       "    domSelector: \"#feder-container-988511\",\n",
       "    viewParams: {\n",
       "        ...{'width': 950, 'height': 600, 'mediaType': 'img', 'fineSearchWithProjection': 1, 'projectMethod': 'umap'},\n",
       "        mediaCallback,\n",
       "    }\n",
       "})\n",
       "        \n",
       "feder.setSearchParams({'k': 5, 'nprobe': 6})\n",
       "feder.searchById(40)\n",
       "        \n",
       "</script>\n",
       "\n",
       "</html> \n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "faiss_feder = get_faiss_feder('faiss.index')\n",
    "faiss_feder.searchById(40)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c6cf0bb",
   "metadata": {},
   "source": [
    "- **Search in HNSWLib and visualization**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69d2cc35",
   "metadata": {},
   "source": [
    "Similar to Faiss, we first define the `get_hnsw_feder` function to read the index file from hnswlib, where the `mediaType` is img, the `mediaUrls` here is the `image`s declared earlier, and the query parameters of HNSW index is `k=5`, `ef_search=6`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c4be49a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "from federpy.federpy import FederPy\n",
    "\n",
    "def get_hnsw_feder(hnsw_index_file_name):\n",
    "    viewParams = {\n",
    "        \"width\": 950,\n",
    "        \"height\": 600,\n",
    "        \"mediaType\": \"img\",\n",
    "        \"mediaUrls\": images,\n",
    "    }\n",
    "    hnsw_federPy = FederPy(hnsw_index_file_name, 'hnswlib', **viewParams)\n",
    "    hnsw_federPy.setSearchParams({\"k\": 5, \"ef_search\": 6})\n",
    "    return hnsw_federPy"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5df64e5",
   "metadata": {},
   "source": [
    "Before search vector with Feder, let's learn about HNSW ([Hierarchical Navigable Small World Graph](https://arxiv.org/abs/1603.09320)) index, which is a graph-based indexing algorithm. It builds a multi-layer navigation structure for an image according to certain rules. In this structure, the upper layers are more sparse and the distances between nodes are farther; the lower layers are denser and the distances between nodes are closer. The search starts from the uppermost layer, finds the node closest to the target in this layer, and then enters the next layer to begin another search. After multiple iterations, it can quickly approach the target position."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3e7e603",
   "metadata": {},
   "source": [
    "Next, we can get hnsw feder and visualize the index. Here is an example of retrieving a image with an id of 40. You can see that the basic information of the index (1000 vectors, including 4 levels) and the information of the process of retrieval vector (130 of these vectors were visited in total) is listed on the left.\n",
    "\n",
    "\n",
    "The entire board shows 4 layers and the retrieval process for each layer. First, find the nearest vectors in the first three layers (Level 3, 2, 1), whihc is colored with red dots. Then 111 vectors are visited in the last layer, and the nearest vector is find. The five results closest to the query vector are indicated by red dots at the Level 0 layer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b0904f1a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<!DOCTYPE html>\n",
       "<html>\n",
       "\n",
       "<head>\n",
       "  <meta charset=\"utf-8\" />\n",
       "  <meta name=\"viewport\" content=\"width=device-width,initial-scale=1\" />\n",
       "  <meta name=\"theme-color\" content=\"#ffffff\" />\n",
       "  <title>Feder</title>\n",
       "</head>\n",
       "\n",
       "<body style=\"background: #000\">\n",
       "  <div id=\"feder-container-5541744\" />\n",
       "</body>\n",
       "\n",
       "<script type=\"module\">\n",
       "\n",
       "\n",
       "import { Feder } from \"https://unpkg.com/@zilliz/feder\"\n",
       "// console.log(Feder)\n",
       "\n",
       "const mediaUrls = 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       "const mediaCallback = (rowId) => rowId in mediaUrls ? mediaUrls[rowId] : null\n",
       "\n",
       "const feder = new Feder({\n",
       "    filePath: \"hnswlib.index\",\n",
       "    source: \"hnswlib\",\n",
       "    domSelector: \"#feder-container-5541744\",\n",
       "    viewParams: {\n",
       "        ...{'width': 950, 'height': 600, 'mediaType': 'img'},\n",
       "        mediaCallback,\n",
       "    }\n",
       "})\n",
       "        \n",
       "feder.setSearchParams({'k': 5, 'ef_search': 6})\n",
       "feder.searchById(40)\n",
       "        \n",
       "</script>\n",
       "\n",
       "</html> \n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hnsw_federPy = get_hnsw_feder('hnswlib.index')\n",
    "hnsw_federPy.searchById(40)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b082d74e",
   "metadata": {},
   "source": [
    "## Normalize the vector and visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6fac5554",
   "metadata": {},
   "source": [
    "In the previous example([Build a Milvus Powered Image Search Engine in Minutes](../reverse_image_search/1_build_image_search_engine.ipynb)) we found that normalizing the vector can improve the accuracy of the reverse image search, so let's take a look about the retrieving process with normalized vectors.\n",
    "\n",
    "First we extract the feature vector of the image and then normalize the vector."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "2f645047",
   "metadata": {},
   "outputs": [],
   "source": [
    "p_embed_norm = (\n",
    "    pipe.input('src')\n",
    "        .flat_map('src', 'img_path', lambda x: glob.glob(x))\n",
    "        .map('img_path', 'img', ops.image_decode.cv2())\n",
    "        .map('img', 'vec', ops.image_embedding.timm(model_name='resnet50'))\n",
    "        .map('vec', 'vec', lambda x: x / np.linalg.norm(x, axis=0))\n",
    "        .output('vec')\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "1ef2b240",
   "metadata": {},
   "outputs": [],
   "source": [
    "vectors_norm = p_embed_norm('train/*/*.JPEG').to_list()\n",
    "vectors_norm = np.array(np.squeeze(vectors_norm ), dtype=\"float32\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a58d1b8",
   "metadata": {},
   "source": [
    "Next insert the normalized vector into Faiss and Hnswlib, then return the corresponding index file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "8a6afc0b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING clustering 1000 points to 128 centroids: please provide at least 4992 training points\n"
     ]
    }
   ],
   "source": [
    "save_faiss_index(vectors_norm , 'faiss_norm.index')\n",
    "save_hnsw_index(vectors_norm , 'hnswlib_norm.index')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f5fcc54",
   "metadata": {},
   "source": [
    "There is the Faiss index after normalization:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "1f253da3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<!DOCTYPE html>\n",
       "<html>\n",
       "\n",
       "<head>\n",
       "  <meta charset=\"utf-8\" />\n",
       "  <meta name=\"viewport\" content=\"width=device-width,initial-scale=1\" />\n",
       "  <meta name=\"theme-color\" content=\"#ffffff\" />\n",
       "  <title>Feder</title>\n",
       "</head>\n",
       "\n",
       "<body style=\"background: #000\">\n",
       "  <div id=\"feder-container-8441633\" />\n",
       "</body>\n",
       "\n",
       "<script type=\"module\">\n",
       "\n",
       "\n",
       "import { Feder } from \"https://unpkg.com/@zilliz/feder\"\n",
       "// console.log(Feder)\n",
       "\n",
       "const mediaUrls = 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       "const mediaCallback = (rowId) => rowId in mediaUrls ? mediaUrls[rowId] : null\n",
       "\n",
       "const feder = new Feder({\n",
       "    filePath: \"faiss_norm.index\",\n",
       "    source: \"faiss\",\n",
       "    domSelector: \"#feder-container-8441633\",\n",
       "    viewParams: {\n",
       "        ...{'width': 950, 'height': 600, 'mediaType': 'img', 'fineSearchWithProjection': 1, 'projectMethod': 'umap'},\n",
       "        mediaCallback,\n",
       "    }\n",
       "})\n",
       "        \n",
       "feder.setSearchParams({'k': 5, 'nprobe': 6})\n",
       "feder.searchById(40)\n",
       "        \n",
       "</script>\n",
       "\n",
       "</html> \n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "faiss_feder_norm = get_faiss_feder('faiss_norm.index')\n",
    "faiss_feder_norm.searchById(40)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63d783fd",
   "metadata": {},
   "source": [
    "There is the HNSW index after normalization:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "c00b6b40",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<!DOCTYPE html>\n",
       "<html>\n",
       "\n",
       "<head>\n",
       "  <meta charset=\"utf-8\" />\n",
       "  <meta name=\"viewport\" content=\"width=device-width,initial-scale=1\" />\n",
       "  <meta name=\"theme-color\" content=\"#ffffff\" />\n",
       "  <title>Feder</title>\n",
       "</head>\n",
       "\n",
       "<body style=\"background: #000\">\n",
       "  <div id=\"feder-container-6771756\" />\n",
       "</body>\n",
       "\n",
       "<script type=\"module\">\n",
       "\n",
       "\n",
       "import { Feder } from \"https://unpkg.com/@zilliz/feder\"\n",
       "// console.log(Feder)\n",
       "\n",
       "const mediaUrls = 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       "const mediaCallback = (rowId) => rowId in mediaUrls ? mediaUrls[rowId] : null\n",
       "\n",
       "const feder = new Feder({\n",
       "    filePath: \"hnswlib_norm.index\",\n",
       "    source: \"hnswlib\",\n",
       "    domSelector: \"#feder-container-6771756\",\n",
       "    viewParams: {\n",
       "        ...{'width': 950, 'height': 600, 'mediaType': 'img'},\n",
       "        mediaCallback,\n",
       "    }\n",
       "})\n",
       "        \n",
       "feder.setSearchParams({'k': 5, 'ef_search': 6})\n",
       "feder.searchById(40)\n",
       "        \n",
       "</script>\n",
       "\n",
       "</html> \n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hnsw_federPy_norm = get_hnsw_feder('hnswlib_norm.index')\n",
    "hnsw_federPy_norm.searchById(40)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a4e593e",
   "metadata": {},
   "source": [
    "# Search with Object Detection"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75fb83c7",
   "metadata": {},
   "source": [
    "In the previous example ([Deep Dive into Real-World Image Search Engine with Towhee](../reverse_image_search/2_deep_dive_image_search.ipynb)), we know that object detection performs well when retrieving partial data, next we compare the retrieval process with and without object detection.\n",
    "\n",
    "The `get_object` function is used to get the image of the object detected by YoLov5, or the image itself if there is no object. First, we can get the feature vector of the same image after detecting object, as well as the feature vector of the original image."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "2402b4a4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Looking in indexes: https://mirrors.aliyun.com/pypi/simple/, https://pypi.ngc.nvidia.com\n",
      "Requirement already satisfied: pyyaml in /home/xuyu/anaconda3/envs/jit/lib/python3.9/site-packages (6.0)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "[notice] A new release of pip available: 22.2.2 -> 23.0\n",
      "[notice] To update, run: pip install --upgrade pip\n",
      "Using cache found in /home/xuyu/.cache/torch/hub/ultralytics_yolov5_master\n",
      "2023-02-15 14:31:41,391 - 139678516758336 - helpers.py-helpers:244 - INFO: Loading pretrained weights from url (https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rsb-weights/resnet50_a1_0-14fe96d1.pth)\n",
      "Loading pretrained weights from url (https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rsb-weights/resnet50_a1_0-14fe96d1.pth)\n",
      "2023-02-15 14:31:41,523 - 139662152685312 - node.py-node:165 - INFO: Begin to run Node-_input\n",
      "Begin to run Node-_input\n",
      "2023-02-15 14:31:41,524 - 139661934589696 - node.py-node:165 - INFO: Begin to run Node-lambda-0\n",
      "Begin to run Node-lambda-0\n",
      "2023-02-15 14:31:41,524 - 139664442779392 - node.py-node:165 - INFO: Begin to run Node-image-decode/cv2-1\n",
      "Begin to run Node-image-decode/cv2-1\n",
      "2023-02-15 14:31:41,525 - 139662161078016 - node.py-node:165 - INFO: Begin to run Node-image-embedding/timm-2\n",
      "2023-02-15 14:31:41,526 - 139662169470720 - node.py-node:165 - INFO: Begin to run Node-lambda-3\n",
      "2023-02-15 14:31:41,526 - 139665726240512 - node.py-node:165 - INFO: Begin to run Node-_output\n",
      "Begin to run Node-image-embedding/timm-2\n",
      "Begin to run Node-lambda-3\n",
      "Begin to run Node-_output\n",
      "2023-02-15 14:31:41,641 - 139664434386688 - node.py-node:165 - INFO: Begin to run Node-_input\n",
      "Begin to run Node-_input\n",
      "2023-02-15 14:31:41,642 - 139662572123904 - node.py-node:165 - INFO: Begin to run Node-lambda-0\n",
      "Begin to run Node-lambda-0\n",
      "2023-02-15 14:31:41,642 - 139662563731200 - node.py-node:165 - INFO: Begin to run Node-image-decode/cv2-1\n",
      "Begin to run Node-image-decode/cv2-1\n",
      "2023-02-15 14:31:41,643 - 139662555338496 - node.py-node:165 - INFO: Begin to run Node-object-detection/yolov5-2\n",
      "Begin to run Node-object-detection/yolov5-2\n",
      "2023-02-15 14:31:41,644 - 139662437906176 - node.py-node:165 - INFO: Begin to run Node-get_object-3\n",
      "Begin to run Node-get_object-3\n",
      "2023-02-15 14:31:41,645 - 139664434386688 - node.py-node:165 - INFO: Begin to run Node-image-embedding/timm-4\n",
      "Begin to run Node-image-embedding/timm-4\n",
      "2023-02-15 14:31:41,648 - 139662429513472 - node.py-node:165 - INFO: Begin to run Node-lambda-5\n",
      "Begin to run Node-lambda-5\n",
      "2023-02-15 14:31:41,648 - 139662421120768 - node.py-node:165 - INFO: Begin to run Node-_output\n",
      "Begin to run Node-_output\n"
     ]
    }
   ],
   "source": [
    "images_obj = glob.glob('./object/*.jpg')\n",
    "\n",
    "def get_object(img, boxes):\n",
    "    if len(boxes) == 0:\n",
    "        return img\n",
    "    max_area = 0\n",
    "    for box in boxes:\n",
    "        x1, y1, x2, y2 = box\n",
    "        area = (x2-x1)*(y2-y1)\n",
    "        if area > max_area:\n",
    "            max_area = area\n",
    "            max_img = img[y1:y2,x1:x2,:]\n",
    "    return max_img\n",
    "\n",
    "p_embed_obj = (\n",
    "    pipe.input('src')\n",
    "        .flat_map('src', 'img_path', lambda x: glob.glob(x))\n",
    "        .map('img_path', 'img', ops.image_decode.cv2())\n",
    "        .map('img', ('boxes', 'class', 'score'), ops.object_detection.yolov5())\n",
    "        .map(('img', 'boxes'), 'object', get_object)\n",
    "        .map('object', 'object_vec', ops.image_embedding.timm(model_name='resnet50'))\n",
    "        .map('object_vec', 'object_vec', lambda x: x / np.linalg.norm(x, axis=0))\n",
    "        .output('object_vec')\n",
    ")\n",
    "\n",
    "dc_img = p_embed_norm('./object/*.jpg').to_list()\n",
    "vectors_img = np.array(np.squeeze(dc_img), dtype=\"float32\")\n",
    "\n",
    "dc_obj = p_embed_obj('./object/*.jpg').to_list()\n",
    "vectors_obj = np.array(np.squeeze(dc_obj), dtype=\"float32\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09a10a46",
   "metadata": {},
   "source": [
    "We first retrieve the original image vector (without object detection), we can see that the closest clusters to the original image are various strange images, and there are 17 various images in the cluster 69 where the nearest center point is located. Then we can click Fine Search, we can see the 5 results about spider, it's same as the [Deep Dive into Image Search](../reverse_image_search/2_deep_dive_image_search.ipynb) notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "8f784ac5",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<!DOCTYPE html>\n",
       "<html>\n",
       "\n",
       "<head>\n",
       "  <meta charset=\"utf-8\" />\n",
       "  <meta name=\"viewport\" content=\"width=device-width,initial-scale=1\" />\n",
       "  <meta name=\"theme-color\" content=\"#ffffff\" />\n",
       "  <title>Feder</title>\n",
       "</head>\n",
       "\n",
       "<body style=\"background: #000\">\n",
       "  <div id=\"feder-container-8447273\" />\n",
       "</body>\n",
       "\n",
       "<script type=\"module\">\n",
       "\n",
       "\n",
       "import { Feder } from \"https://unpkg.com/@zilliz/feder\"\n",
       "// console.log(Feder)\n",
       "\n",
       "const mediaUrls = 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       "const mediaCallback = (rowId) => rowId in mediaUrls ? mediaUrls[rowId] : null\n",
       "\n",
       "const feder = new Feder({\n",
       "    filePath: \"faiss_norm.index\",\n",
       "    source: \"faiss\",\n",
       "    domSelector: \"#feder-container-8447273\",\n",
       "    viewParams: {\n",
       "        ...{'width': 950, 'height': 600, 'mediaType': 'img', 'fineSearchWithProjection': 1, 'projectMethod': 'umap'},\n",
       "        mediaCallback,\n",
       "    }\n",
       "})\n",
       "        \n",
       "feder.setSearchParams({'k': 5, 'nprobe': 6})\n",
       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       "        \n",
       "</script>\n",
       "\n",
       "</html> \n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "faiss_feder_norm = get_faiss_feder('faiss_norm.index')\n",
    "faiss_feder_norm.searchByVec(vectors_img[0], images_obj[0]) #Search without object detection"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b885ce3b",
   "metadata": {},
   "source": [
    "The vectors with object detection are all images of cars in the nearest cluster0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "9d0daa40",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<!DOCTYPE html>\n",
       "<html>\n",
       "\n",
       "<head>\n",
       "  <meta charset=\"utf-8\" />\n",
       "  <meta name=\"viewport\" content=\"width=device-width,initial-scale=1\" />\n",
       "  <meta name=\"theme-color\" content=\"#ffffff\" />\n",
       "  <title>Feder</title>\n",
       "</head>\n",
       "\n",
       "<body style=\"background: #000\">\n",
       "  <div id=\"feder-container-7092330\" />\n",
       "</body>\n",
       "\n",
       "<script type=\"module\">\n",
       "\n",
       "\n",
       "import { Feder } from \"https://unpkg.com/@zilliz/feder\"\n",
       "// console.log(Feder)\n",
       "\n",
       "const mediaUrls = 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       "const mediaCallback = (rowId) => rowId in mediaUrls ? mediaUrls[rowId] : null\n",
       "\n",
       "const feder = new Feder({\n",
       "    filePath: \"faiss_norm.index\",\n",
       "    source: \"faiss\",\n",
       "    domSelector: \"#feder-container-7092330\",\n",
       "    viewParams: {\n",
       "        ...{'width': 950, 'height': 600, 'mediaType': 'img', 'fineSearchWithProjection': 1, 'projectMethod': 'umap'},\n",
       "        mediaCallback,\n",
       "    }\n",
       "})\n",
       "        \n",
       "feder.setSearchParams({'k': 5, 'nprobe': 6})\n",
       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       "        \n",
       "</script>\n",
       "\n",
       "</html> \n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "faiss_feder_norm = get_faiss_feder('faiss_norm.index')\n",
    "faiss_feder_norm.searchByVec(vectors_obj[0], images_obj[0]) #Search with object detection"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5dae8c01",
   "metadata": {},
   "source": [
    "# Cross-modal Search (Text-Image)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ff4a92a",
   "metadata": {},
   "source": [
    "The visualization of cross-modal search is the last and most interesting, it uses towhee [clip Operator](https://towhee.io/towhee/clip) to extract feature vectors of images and text, if the content of the image and text description are similar, their vector distances will also be very close.\n",
    "\n",
    "First, we use cilp to generate feature vectors for all images, and we need to set `modality='image'`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "bf7e48f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "p_embed_image = (\n",
    "    pipe.input('src')\n",
    "        .flat_map('src', 'img_path', lambda x: glob.glob(x))\n",
    "        .map('img_path', 'img', ops.image_decode.cv2())\n",
    "        .map('img', 'vec', ops.image_text_embedding.clip(model_name='clip_vit_base_patch16', modality='image'))\n",
    "        .map('vec', 'vec', lambda x: x / np.linalg.norm(x, axis=0))\n",
    "        .output('vec')\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "a921f325",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-02-15 14:32:30,983 - 139657015502592 - node.py-node:165 - INFO: Begin to run Node-_input\n",
      "Begin to run Node-_input\n",
      "2023-02-15 14:32:30,985 - 139656968533760 - node.py-node:165 - INFO: Begin to run Node-lambda-0\n",
      "2023-02-15 14:32:30,986 - 139656960141056 - node.py-node:165 - INFO: Begin to run Node-image-decode/cv2-1\n",
      "Begin to run Node-lambda-0\n",
      "Begin to run Node-image-decode/cv2-1\n",
      "2023-02-15 14:32:30,987 - 139656951748352 - node.py-node:165 - INFO: Begin to run Node-image-text-embedding/clip-2\n",
      "Begin to run Node-image-text-embedding/clip-2\n",
      "2023-02-15 14:32:30,989 - 139656943355648 - node.py-node:165 - INFO: Begin to run Node-lambda-3\n",
      "2023-02-15 14:32:30,994 - 139656934962944 - node.py-node:165 - INFO: Begin to run Node-_output\n",
      "Begin to run Node-lambda-3\n",
      "Begin to run Node-_output\n"
     ]
    }
   ],
   "source": [
    "dc_img = p_embed_image('train/*/*.JPEG').to_list()\n",
    "vectors_img = np.array(np.squeeze(dc_img), dtype=\"float32\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a32734b1",
   "metadata": {},
   "source": [
    "Insert all image vectors into Hnswlib and return the index file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "eacc708c",
   "metadata": {},
   "outputs": [],
   "source": [
    "save_hnsw_index(vectors_img, 'hnswlib_cm.index')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b9a5df9",
   "metadata": {},
   "source": [
    "Next, we will search for images using text, first generating a vector of text and then using `searchByVec` to search in Feder. We can use the Cilp operator to get the feature vector of 'A white dog' and \"a black dog\" by setting modality='text'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "d26df2f3",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-02-15 14:35:22,489 - 139653973780224 - node.py-node:165 - INFO: Begin to run Node-_input\n",
      "Begin to run Node-_input\n",
      "2023-02-15 14:35:22,491 - 139653982172928 - node.py-node:165 - INFO: Begin to run Node-lambda-0\n",
      "2023-02-15 14:35:22,492 - 139653990565632 - node.py-node:165 - INFO: Begin to run Node-image-text-embedding/clip-1\n",
      "Begin to run Node-lambda-0\n",
      "2023-02-15 14:35:22,493 - 139653998958336 - node.py-node:165 - INFO: Begin to run Node-lambda-2\n",
      "Begin to run Node-image-text-embedding/clip-1\n",
      "2023-02-15 14:35:22,494 - 139656631867136 - node.py-node:165 - INFO: Begin to run Node-_output\n",
      "Begin to run Node-lambda-2\n",
      "Begin to run Node-_output\n"
     ]
    }
   ],
   "source": [
    "p_embed_text = (\n",
    "    pipe.input('src')\n",
    "        .flat_map('src', 'text', lambda x: x)\n",
    "        .map('text', 'vec', ops.image_text_embedding.clip(model_name='clip_vit_base_patch16', modality='text'))\n",
    "        .map('vec', 'vec', lambda x: x / np.linalg.norm(x, axis=0))\n",
    "        .output('vec')\n",
    ")\n",
    "\n",
    "dc_text = p_embed_text(['A white dog.', 'A blcak dog']).to_list()\n",
    "vectors_text = np.array(np.squeeze(dc_text), dtype=\"float32\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "502c04e9",
   "metadata": {},
   "source": [
    "To search 'A white dog' in Hnswlib, the retrieval process is as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "b5377391",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<!DOCTYPE html>\n",
       "<html>\n",
       "\n",
       "<head>\n",
       "  <meta charset=\"utf-8\" />\n",
       "  <meta name=\"viewport\" content=\"width=device-width,initial-scale=1\" />\n",
       "  <meta name=\"theme-color\" content=\"#ffffff\" />\n",
       "  <title>Feder</title>\n",
       "</head>\n",
       "\n",
       "<body style=\"background: #000\">\n",
       "  <div id=\"feder-container-4619438\" />\n",
       "</body>\n",
       "\n",
       "<script type=\"module\">\n",
       "\n",
       "\n",
       "import { Feder } from \"https://unpkg.com/@zilliz/feder\"\n",
       "// console.log(Feder)\n",
       "\n",
       "const mediaUrls = 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       "const mediaCallback = (rowId) => rowId in mediaUrls ? mediaUrls[rowId] : null\n",
       "\n",
       "const feder = new Feder({\n",
       "    filePath: \"hnswlib_cm.index\",\n",
       "    source: \"hnswlib\",\n",
       "    domSelector: \"#feder-container-4619438\",\n",
       "    viewParams: {\n",
       "        ...{'width': 950, 'height': 600, 'mediaType': 'img'},\n",
       "        mediaCallback,\n",
       "    }\n",
       "})\n",
       "        \n",
       "feder.setSearchParams({'k': 5, 'ef_search': 6})\n",
       "feder.search([0.037347693,-0.006965721,-0.004341051,0.033502143,0.0411059,0.065238066,-0.044761352,-0.00037502885,0.0059341406,-0.022511514,0.017566446,-0.025200872,0.008165511,-0.0131794615,-0.014686442,0.010380234,-0.055645872,-0.039336395,-0.04272785,-0.03640774,0.010054458,0.045796406,-0.028632132,0.021414494,-0.017928945,-0.056898773,-0.00787823,-0.013436596,-0.04110247,-0.0061950376,-0.021141125,-0.033997007,0.01626282,-0.012626746,0.030225232,-0.026126726,-0.004161484,0.0014889815,-0.021339685,-0.0099182315,-0.0024940574,-0.044440947,-0.046598904,-0.058446143,-0.007905981,0.011812864,0.0014689267,-0.026810097,-0.00265212,0.0025860074,0.005071881,-0.036407307,-0.0076983543,0.024528809,-0.035759818,-0.08501136,0.010684972,0.024842946,0.009251757,0.032326106,0.02222959,0.04021792,-0.00239663,0.028214585,0.025240054,0.061686113,-0.049733978,0.00874218,0.020143704,-0.004920805,0.013966353,0.060178548,0.0032888178,0.034650065,-0.003062064,-0.008711008,-0.029777557,-0.07932487,0.009191824,-0.023936126,0.03100032,0.0010811737,-0.040497486,-0.009346686,-0.02383035,-0.012218498,-0.026710168,0.00844546,0.045380134,0.04704745,-0.014225169,-0.015366149,-0.024970625,0.03873628,-0.046324026,-0.06100343,-0.14768295,-0.006107968,-0.01786453,-0.022557305,0.001153605,-0.00977961,0.020156376,0.0246799,-0.028811393,-0.02228404,0.049276073,-0.026129272,-0.03662306,0.007258505,0.04926615,0.04279467,-0.00078832573,0.03218393,0.051366884,-0.019433964,0.0513317,0.036779728,-0.021197174,0.04472586,-0.023209235,0.01666929,-0.003700588,0.009805699,0.008574857,-0.0064233714,0.0138060935,-0.0057349484,-0.024822365,0.028816588,-0.0020305358,0.001505032,0.00022379265,0.0050510108,0.039062917,-0.0080827605,-0.008822801,0.0011673776,-0.028421946,-0.045609757,0.040453058,-0.018264383,0.038208783,-0.008739452,-0.002020168,-0.051425163,-0.0002062972,0.036603674,-0.00074123766,0.01821225,0.01360579,0.05322971,0.03622793,-0.046398714,-0.00071867957,-0.030130176,0.053990517,0.013883195,0.025235139,0.005154739,-0.043397333,-0.026525207,-0.023360113,-0.018153602,0.02339647,0.04920791,-0.0032412955,-0.003218479,0.015479616,0.022321599,0.012744903,0.035438053,-0.040130798,-0.03293829,0.046865862,0.064787716,-0.011928369,0.014280623,0.018717576,0.050400205,-0.011859076,-0.04451861,0.009248654,0.03657079,0.011789098,0.007093291,0.013265989,0.02395454,0.0024864632,0.045936402,0.0065271454,0.010208029,-0.00767565,0.014754546,0.016385272,-0.009860294,-0.024502188,-0.041506406,0.0060054925,0.02105237,0.032754626,0.03593431,0.014664292,-0.0074129924,-0.004701894,0.0064351265,0.029174786,0.04038702,0.007422662,0.031178275,0.016973883,-0.045089018,0.017410949,-0.012066505,0.012636753,0.013283662,0.00993797,-0.016252115,-0.009448128,0.018914267,0.03834483,0.015785018,0.006371313,-0.0279948,0.0017335821,0.005795035,-0.014127312,-0.0229758,0.0047245184,0.032837626,-0.012397828,0.0032991432,0.073545404,0.019789176,-0.018687887,0.008783099,0.0274197,-0.010525827,-0.027349666,-0.008351385,0.051031686,-0.026376203,-0.00010630055,-0.016805492,0.0011615618,0.031801105,0.03436934,0.01716825,-0.0012006239,0.040294405,-0.028723668,-0.0076117143,0.016061647,-0.029231505,0.012939683,-0.006509157,-0.007088764,0.013477701,0.011680601,0.004237275,-0.012393517,-0.0026677963,0.018769387,-0.015807357,0.03162348,0.017180506,0.03188482,0.05624626,0.05254555,-0.005594902,0.029580066,0.0032468704,0.039024357,-0.028845327,0.018162528,-0.011734012,-0.006823592,0.024647439,-0.0047856695,0.008555991,-0.0027427606,-0.010077783,-0.029958928,-0.037524115,0.032646943,-0.0680789,0.012110118,-0.03387043,-0.0027200964,0.002268733,-0.027597502,-0.009355774,0.13481106,-0.037988592,-0.011176583,-0.014718755,0.006922612,-0.023862064,-0.03793753,-0.01382751,-0.023416033,0.0077414704,0.035669114,-0.0027647873,0.02461052,-0.026750524,-0.009273819,0.0030619223,0.027171921,0.009855755,-0.0073338705,-0.041094247,-0.0034228847,-0.012256087,0.008202605,-0.011455222,-0.0133791445,0.004599519,-0.01232393,0.053972308,-0.019632269,-0.039262835,0.020159777,-0.0004860629,-0.036752686,-0.010248167,-0.019379713,0.0002654246,-0.013505131,-0.027345495,-0.025557443,0.01872818,0.021924164,0.03947897,0.0070335697,0.022166321,0.02175692,-0.015250649,-0.006998159,0.006583166,-0.02945337,-0.04647238,-0.064878784,0.013010904,0.009454482,-0.029850576,-0.001810523,0.020929009,-0.026906487,0.023754431,0.045675173,-0.0052217166,-0.016099622,0.010667945,-0.033314757,0.020785658,0.014292948,-0.042726282,-0.023084393,-0.011667852,-0.021641158,0.03436454,0.7331031,0.006974951,0.05650938,0.0066262297,0.043094676,0.0033099381,-0.0037417398,-0.007747729,-0.0043768864,0.016125154,0.012023691,0.035567656,-0.022728411,0.0072016385,0.015940024,-0.03887434,0.02560679,-0.0066221724,-0.03264183,-0.03364879,-0.004566847,0.011310963,-0.00060184737,0.0565167,-0.056882877,0.0021200748,-0.012286054,0.046583015,0.020957356,-0.011727046,0.010804664,-0.027916249,-0.0034595719,0.030312644,-0.055533845,0.011316576,-0.025398443,0.02284554,-0.0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       "        \n",
       "</script>\n",
       "\n",
       "</html> \n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hnsw_federPy_cm = get_hnsw_feder('hnswlib_cm.index')\n",
    "hnsw_federPy_cm.searchByVec(vectors_text[0]) #search the white dog"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f4f9afc",
   "metadata": {},
   "source": [
    "Search for 'A Black dog' in Hnswlib as follows, you can see that although the results are all pictures of dogs, the retrieval process is quite different.\n",
    "\n",
    "When retrieving white dogs, 147 vectors were visited in the last layer, and images of white elements were found in the second layer(Level 2). When retrieving black dogs, 89 vectors were visited in the last layer, and images with black elements were found in the second layer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "1b5a66d6",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<!DOCTYPE html>\n",
       "<html>\n",
       "\n",
       "<head>\n",
       "  <meta charset=\"utf-8\" />\n",
       "  <meta name=\"viewport\" content=\"width=device-width,initial-scale=1\" />\n",
       "  <meta name=\"theme-color\" content=\"#ffffff\" />\n",
       "  <title>Feder</title>\n",
       "</head>\n",
       "\n",
       "<body style=\"background: #000\">\n",
       "  <div id=\"feder-container-6457558\" />\n",
       "</body>\n",
       "\n",
       "<script type=\"module\">\n",
       "\n",
       "\n",
       "import { Feder } from \"https://unpkg.com/@zilliz/feder\"\n",
       "// console.log(Feder)\n",
       "\n",
       "const mediaUrls = 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       "const mediaCallback = (rowId) => rowId in mediaUrls ? mediaUrls[rowId] : null\n",
       "\n",
       "const feder = new Feder({\n",
       "    filePath: \"hnswlib_cm.index\",\n",
       "    source: \"hnswlib\",\n",
       "    domSelector: \"#feder-container-6457558\",\n",
       "    viewParams: {\n",
       "        ...{'width': 950, 'height': 600, 'mediaType': 'img'},\n",
       "        mediaCallback,\n",
       "    }\n",
       "})\n",
       "        \n",
       "feder.setSearchParams({'k': 5, 'ef_search': 6})\n",
       "feder.search([0.043480802,-0.03544126,0.01035842,0.027702795,0.03025557,0.04476333,-0.019132178,-0.0020869137,0.03413664,-0.046987582,-0.0075131147,-0.026591808,0.025377152,-0.01802945,0.019494586,0.039503995,0.0008829255,-0.01061111,-0.015010666,0.009358156,-0.010178298,-0.01727596,-0.07113412,0.01747216,-0.025245016,-0.04471037,-0.026360016,0.042424567,-0.028233835,-0.007112666,0.0040866095,-0.021826958,0.000900008,-0.019397188,0.009748492,-0.002771238,0.032811534,0.011232198,0.02742741,-0.0104291225,-0.036953654,-0.01389876,0.03390498,-0.018137692,0.014557409,-0.013973349,-0.018196493,0.0026219096,-0.002358026,-0.034769624,0.02233574,-0.0026851853,-0.021611506,-0.009773804,-0.01739124,-0.020119501,-0.0030789673,0.00068877987,0.042705562,0.0006652588,0.034850925,0.012133462,-0.010040267,-0.00552604,0.004598101,0.04387237,-0.018040359,-0.009591956,0.0027726677,-0.03195172,0.02375119,0.06078786,-0.025355294,0.004677272,0.002267878,0.0051574684,-0.003968304,-0.04663293,0.0018615624,-0.018895045,0.043106884,0.0320937,-0.03882841,-0.011753435,-0.02911779,0.0097843185,-0.012084446,-0.008481274,-0.006397892,0.016519362,0.0050872387,-0.021885319,-0.0015488728,0.020995703,-0.0491381,-0.02956169,-0.13945693,0.0062749237,-0.007647661,-0.044591308,0.02659116,-0.008802869,0.025750035,0.025062956,0.02402367,-0.0040890793,0.03872237,-0.032520484,-0.028945582,-0.0078252135,0.057920124,0.014213509,0.015758574,0.016366031,0.043646805,-0.009936046,0.0032574,0.042079434,-0.016990663,0.026154324,-0.014536545,0.006383823,0.007915599,0.026502457,0.044315152,-0.02845057,0.021430522,0.0010522372,-0.010005073,-0.0023199937,0.047155898,-0.011751875,-0.0052007483,-0.035585035,0.037239973,-0.039425712,-0.03909114,0.008563189,-0.035556953,0.0135834515,0.03879985,-0.018848578,0.05251019,-0.022331685,-0.02345418,-0.036066703,-0.008844425,0.034678187,-0.0056387484,0.0010645952,-0.017326228,0.025322203,0.002455397,0.0072100293,-0.011924602,0.0015130225,-0.0012756261,0.009638184,0.0085777445,-0.018044654,-0.035266843,0.00027383582,0.012070947,0.02045925,-0.013675334,0.00546896,-0.031384625,0.077778384,0.022902343,0.03903496,-0.0072218454,-0.01408448,-0.019714419,0.008565516,0.007422737,-0.007195012,0.012421329,-0.01805296,0.015423498,0.028714076,0.021442164,-0.0034113182,-0.008815353,0.021248663,-0.018079223,0.03718244,0.031788163,0.029642388,-0.004977135,-0.0040847114,0.013067534,-0.00017834535,-0.001398822,-0.021631645,0.00048053585,0.011855572,-0.040268015,-0.0066328216,-0.03017853,0.036493585,0.023396472,0.016787317,0.019606156,-0.0005057085,0.03878187,-0.05725773,-0.014461423,0.0061116912,-0.007084138,0.013323735,-0.020374084,-0.009668372,0.019526038,-0.009546275,0.039626464,-0.024188984,0.013724111,-0.047424097,-0.011141359,-0.004629667,0.008615117,0.010343324,-0.015545757,0.027565708,-0.056343842,0.006263494,0.013822309,0.018704176,0.0048459433,-0.0075493646,-0.010318218,-0.012649976,0.04164311,0.01079722,-0.0038392213,-0.02980528,0.018214874,-0.021273829,-0.05990515,-0.0396991,0.026495483,-0.036408782,0.020796886,-0.00060188817,-0.006614515,0.037223347,0.017029263,0.029833384,0.010269095,0.06135544,0.006613116,-0.034222443,0.010901819,0.0041387407,0.036022965,-0.0032496213,-0.01948189,-0.017774006,0.0034059586,0.015963344,0.003596835,0.013421298,0.01648826,-0.011782125,0.025199352,0.03850484,0.025837231,0.0093598375,-0.011568509,0.02086085,0.012605551,0.04010166,0.009176281,-0.0054224944,0.049751826,-0.037916474,-0.037997752,-0.021769345,-0.025161337,0.029108478,-0.027178861,0.0012801889,-0.01862282,-0.02121586,0.028171584,-0.03752928,0.0095203435,0.0072739427,0.0036844986,-0.013533421,-0.04540593,-0.012243834,0.12599297,0.0028721197,-0.018943395,-0.014129313,0.01533534,0.0046795383,-0.020585865,-0.032609474,-0.01938934,0.021726368,-0.023864841,0.021307021,0.01310108,-0.061468486,-0.032711968,-0.020352036,0.013930121,-0.018182935,-0.03725673,-0.043496445,-0.021624258,-0.0059954505,-0.017081581,0.0038172165,-0.008782999,-0.024508046,0.037895758,0.027341733,-0.01363186,-0.0027548214,0.0031406675,0.00090989575,0.010606067,0.001171032,-0.025405286,-0.020745045,0.019277193,0.006244644,-0.0204418,-0.0050758165,0.0294486,0.034017757,0.03865568,-0.005071291,0.0015496803,-0.027335213,-0.015892193,0.018378733,-0.0011751252,-0.028058274,-0.0071986946,-0.015945874,-0.012821273,0.016207702,-0.013258439,-0.0036956822,-0.027239723,0.012105197,0.049723163,0.00633779,-0.021836694,-0.0062648403,-0.03560102,-0.010608912,-0.0064439196,-0.03020524,-0.042236134,0.02173731,-0.022125484,0.028950261,0.7919466,0.016646564,0.06772789,-0.008992199,-0.019719843,-0.011957113,0.028564246,-0.016290585,-0.01840803,-0.003587365,0.0048117987,-0.0221237,0.017693156,0.040212285,0.020252453,-0.0077908803,-0.012943205,-0.011176798,0.029232848,-0.007161752,-0.0111214565,0.023292493,0.033372674,0.019244466,-0.0050690966,0.018602746,0.020708237,-0.0031002054,-0.013540854,0.03174215,-0.0017828833,-0.0099449465,-0.0056787897,0.0071561295,-0.027753178,0.011318985,0.016934123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       "        \n",
       "</script>\n",
       "\n",
       "</html> \n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hnsw_federPy_cm = get_hnsw_feder('hnswlib_cm.index')\n",
    "hnsw_federPy_cm.searchByVec(vectors_text[1]) #search the black dog"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b2027d3d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
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    "name": "ipython",
    "version": 3
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   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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